


'''simple docstring'''


import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
				UpperCAmelCase_						:  List[Any]				  =						argparse.ArgumentParser()

				parser.add_argument(
				    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
				)
				# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
				parser.add_argument(
				    '--original_config_file',
				    default=None,
				    type=str,
				    help='The YAML config file corresponding to the original architecture.',
				)
				parser.add_argument(
				    '--num_in_channels',
				    default=None,
				    type=int,
				    help='The number of input channels. If `None` number of input channels will be automatically inferred.',
				)
				parser.add_argument(
				    '--scheduler_type',
				    default='pndm',
				    type=str,
				    help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
				)
				parser.add_argument(
				    '--pipeline_type',
				    default=None,
				    type=str,
				    help=(
				        'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
				        '. If `None` pipeline will be automatically inferred.'
				    ),
				)
				parser.add_argument(
				    '--image_size',
				    default=None,
				    type=int,
				    help=(
				        'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
				        ' Base. Use 768 for Stable Diffusion v2.'
				    ),
				)
				parser.add_argument(
				    '--prediction_type',
				    default=None,
				    type=str,
				    help=(
				        'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
				        ' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
				    ),
				)
				parser.add_argument(
				    '--extract_ema',
				    action='store_true',
				    help=(
				        'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
				        ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
				        ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
				    ),
				)
				parser.add_argument(
				    '--upcast_attention',
				    action='store_true',
				    help=(
				        'Whether the attention computation should always be upcasted. This is necessary when running stable'
				        ' diffusion 2.1.'
				    ),
				)
				parser.add_argument(
				    '--from_safetensors',
				    action='store_true',
				    help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
				)
				parser.add_argument(
				    '--to_safetensors',
				    action='store_true',
				    help='Whether to store pipeline in safetensors format or not.',
				)
				parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
				parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
				parser.add_argument(
				    '--stable_unclip',
				    type=str,
				    default=None,
				    required=False,
				    help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
				)
				parser.add_argument(
				    '--stable_unclip_prior',
				    type=str,
				    default=None,
				    required=False,
				    help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
				)
				parser.add_argument(
				    '--clip_stats_path',
				    type=str,
				    help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
				    required=False,
				)
				parser.add_argument(
				    '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
				)
				parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
				parser.add_argument(
				    '--vae_path',
				    type=str,
				    default=None,
				    required=False,
				    help='Set to a path, hub id to an already converted vae to not convert it again.',
				)
				UpperCAmelCase_						:  int				  =						parser.parse_args()

				UpperCAmelCase_						:  int				  =						download_from_original_stable_diffusion_ckpt(
				    checkpoint_path=args.checkpoint_path,
				    original_config_file=args.original_config_file,
				    image_size=args.image_size,
				    prediction_type=args.prediction_type,
				    model_type=args.pipeline_type,
				    extract_ema=args.extract_ema,
				    scheduler_type=args.scheduler_type,
				    num_in_channels=args.num_in_channels,
				    upcast_attention=args.upcast_attention,
				    from_safetensors=args.from_safetensors,
				    device=args.device,
				    stable_unclip=args.stable_unclip,
				    stable_unclip_prior=args.stable_unclip_prior,
				    clip_stats_path=args.clip_stats_path,
				    controlnet=args.controlnet,
				    vae_path=args.vae_path,
				)

				if args.half:
								pipe.to(torch_dtype=torch.floataa)

				if args.controlnet:
								# only save the controlnet model
								pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
				else:
								pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_						:  Dict				  =						{
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  Optional[Any]				  =						['MobileBertTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  Optional[Any]				  =						[
				    'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MobileBertForMaskedLM',
				    'MobileBertForMultipleChoice',
				    'MobileBertForNextSentencePrediction',
				    'MobileBertForPreTraining',
				    'MobileBertForQuestionAnswering',
				    'MobileBertForSequenceClassification',
				    'MobileBertForTokenClassification',
				    'MobileBertLayer',
				    'MobileBertModel',
				    'MobileBertPreTrainedModel',
				    'load_tf_weights_in_mobilebert',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  List[str]				  =						[
				    'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFMobileBertForMaskedLM',
				    'TFMobileBertForMultipleChoice',
				    'TFMobileBertForNextSentencePrediction',
				    'TFMobileBertForPreTraining',
				    'TFMobileBertForQuestionAnswering',
				    'TFMobileBertForSequenceClassification',
				    'TFMobileBertForTokenClassification',
				    'TFMobileBertMainLayer',
				    'TFMobileBertModel',
				    'TFMobileBertPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_mobilebert import (
				    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileBertConfig,
				    MobileBertOnnxConfig,
				)
				from .tokenization_mobilebert import MobileBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mobilebert_fast import MobileBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilebert import (
								    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileBertForMaskedLM,
								    MobileBertForMultipleChoice,
								    MobileBertForNextSentencePrediction,
								    MobileBertForPreTraining,
								    MobileBertForQuestionAnswering,
								    MobileBertForSequenceClassification,
								    MobileBertForTokenClassification,
								    MobileBertLayer,
								    MobileBertModel,
								    MobileBertPreTrainedModel,
								    load_tf_weights_in_mobilebert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_mobilebert import (
								    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFMobileBertForMaskedLM,
								    TFMobileBertForMultipleChoice,
								    TFMobileBertForNextSentencePrediction,
								    TFMobileBertForPreTraining,
								    TFMobileBertForQuestionAnswering,
								    TFMobileBertForSequenceClassification,
								    TFMobileBertForTokenClassification,
								    TFMobileBertMainLayer,
								    TFMobileBertModel,
								    TFMobileBertPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase_						:  str				  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




